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DC poleHodnotaJazyk
dc.contributor.authorHemani, Mayur
dc.contributor.authorSinha, Abhishek
dc.contributor.authorKrishnamurthy, Balaji
dc.contributor.editorSkala, Václav
dc.date.accessioned2019-10-22T09:13:21Z
dc.date.available2019-10-22T09:13:21Z
dc.date.issued2019
dc.identifier.citationWSCG 2019: full papers proceedings: 27. International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision, p. 37-43.en
dc.identifier.isbn978-80-86943-37-4 (CD/-ROM)
dc.identifier.issn2464–4617 (print)
dc.identifier.issn2464-4625 (CD/DVD)
dc.identifier.urihttp://hdl.handle.net/11025/35607
dc.format7 s.cs
dc.format.mimetypeapplication/odt
dc.language.isoenen
dc.publisherVáclav Skala - UNION Agencycs
dc.rights© Václav Skala - UNION Agencycs
dc.subjectneuronové sítěcs
dc.subjectmanipulace s obrázkycs
dc.subjectstylizace obrazucs
dc.subjectskicacs
dc.titleStylized Sketch Generation using Convolutional Networksen
dc.typekonferenční příspěvekcs
dc.typeconferenceObjecten
dc.rights.accessopenAccessen
dc.type.versionpublishedVersionen
dc.description.abstract-translatedThe task of synthesizing sketches from photographs has been pursued with image processing methods and supervised learning based approaches. The former lack flexibility and the latter require large quantities of ground-truth data which is hard to obtain because of the manual effort required. We present a convolutional neural network based framework for sketch generation that does not require ground-truth data for training and produces various styles of sketches. The method combines simple analytic loss functions that correspond to characteristics of the sketch. The network is trained on and evaluated for human face images. Several stylized variations of sketches are obtained by varying the parameters of the loss functions. The paper also discusses the implicit abstraction afforded by the deep convolutional network approach which results in high quality sketch output.en
dc.subject.translatedneural-networksen
dc.subject.translatedimage manipulationen
dc.subject.translatedimage stylizationen
dc.subject.translatedsketch styleen
dc.identifier.doihttps://doi.org/10.24132/CSRN.2019.2901.1.5
dc.type.statusPeer-revieweden
Vyskytuje se v kolekcích:WSCG 2019: Full Papers Proceedings

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